Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations8220
Missing cells8986
Missing cells (%)5.8%
Duplicate rows580
Duplicate rows (%)7.1%
Total size in memory1.2 MiB
Average record size in memory152.0 B

Variable types

Categorical5
Numeric12
DateTime1
Text1

Alerts

uc_repo has constant value "SAREC-Lab/sUAS-UseCases" Constant
Dataset has 580 (7.1%) duplicate rowsDuplicates
seq_origin is highly overall correlated with seq_temperature and 6 other fieldsHigh correlation
seq_temperature is highly overall correlated with seq_origin and 4 other fieldsHigh correlation
seq_top_p is highly overall correlated with seq_origin and 4 other fieldsHigh correlation
uc_date is highly overall correlated with seq_origin and 6 other fieldsHigh correlation
uc_num_of_alt_scenarions is highly overall correlated with uc_num_of_err_scenarionsHigh correlation
uc_num_of_err_scenarions is highly overall correlated with seq_origin and 5 other fieldsHigh correlation
uc_origin is highly overall correlated with seq_origin and 6 other fieldsHigh correlation
uc_temperature is highly overall correlated with seq_origin and 2 other fieldsHigh correlation
uc_top_p is highly overall correlated with seq_origin and 2 other fieldsHigh correlation
uc_date is highly imbalanced (78.8%) Imbalance
uc_origin is highly imbalanced (84.0%) Imbalance
seq_origin is highly imbalanced (84.0%) Imbalance
seq_num_of_opt is highly imbalanced (93.2%) Imbalance
uc_repo has 8028 (97.7%) missing values Missing
seq_date has 766 (9.3%) missing values Missing
seq_repo has 192 (2.3%) missing values Missing
uc_temperature has 192 (2.3%) zeros Zeros
uc_top_p has 192 (2.3%) zeros Zeros
uc_num_of_alt_scenarions has 7553 (91.9%) zeros Zeros
uc_num_of_err_scenarions has 7733 (94.1%) zeros Zeros
seq_temperature has 8028 (97.7%) zeros Zeros
seq_top_p has 8028 (97.7%) zeros Zeros
seq_actors has 581 (7.1%) zeros Zeros
seq_num_of_alt has 6292 (76.5%) zeros Zeros
seq_num_of_loop has 7757 (94.4%) zeros Zeros

Reproduction

Analysis started2025-04-29 09:34:32.256218
Analysis finished2025-04-29 09:34:42.074154
Duration9.82 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

uc_date
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.3 KiB
2025-04-28
7809 
2025-04-29
 
219
2020-12-19
 
192

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters82200
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-19
2nd row2020-12-19
3rd row2020-12-19
4th row2020-12-19
5th row2020-12-19

Common Values

ValueCountFrequency (%)
2025-04-28 7809
95.0%
2025-04-29 219
 
2.7%
2020-12-19 192
 
2.3%

Length

2025-04-29T11:34:42.114506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-29T11:34:42.159262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2025-04-28 7809
95.0%
2025-04-29 219
 
2.7%
2020-12-19 192
 
2.3%

Most occurring characters

ValueCountFrequency (%)
2 24660
30.0%
0 16440
20.0%
- 16440
20.0%
5 8028
 
9.8%
4 8028
 
9.8%
8 7809
 
9.5%
9 411
 
0.5%
1 384
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 82200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 24660
30.0%
0 16440
20.0%
- 16440
20.0%
5 8028
 
9.8%
4 8028
 
9.8%
8 7809
 
9.5%
9 411
 
0.5%
1 384
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 82200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 24660
30.0%
0 16440
20.0%
- 16440
20.0%
5 8028
 
9.8%
4 8028
 
9.8%
8 7809
 
9.5%
9 411
 
0.5%
1 384
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 82200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 24660
30.0%
0 16440
20.0%
- 16440
20.0%
5 8028
 
9.8%
4 8028
 
9.8%
8 7809
 
9.5%
9 411
 
0.5%
1 384
 
0.5%

uc_origin
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.3 KiB
chatgpt
8028 
github
 
192

Length

Max length7
Median length7
Mean length6.9766423
Min length6

Characters and Unicode

Total characters57348
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgithub
2nd rowgithub
3rd rowgithub
4th rowgithub
5th rowgithub

Common Values

ValueCountFrequency (%)
chatgpt 8028
97.7%
github 192
 
2.3%

Length

2025-04-29T11:34:42.202460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-29T11:34:42.233459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
chatgpt 8028
97.7%
github 192
 
2.3%

Most occurring characters

ValueCountFrequency (%)
t 16248
28.3%
g 8220
14.3%
h 8220
14.3%
c 8028
14.0%
a 8028
14.0%
p 8028
14.0%
i 192
 
0.3%
u 192
 
0.3%
b 192
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 57348
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 16248
28.3%
g 8220
14.3%
h 8220
14.3%
c 8028
14.0%
a 8028
14.0%
p 8028
14.0%
i 192
 
0.3%
u 192
 
0.3%
b 192
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 57348
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 16248
28.3%
g 8220
14.3%
h 8220
14.3%
c 8028
14.0%
a 8028
14.0%
p 8028
14.0%
i 192
 
0.3%
u 192
 
0.3%
b 192
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 57348
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 16248
28.3%
g 8220
14.3%
h 8220
14.3%
c 8028
14.0%
a 8028
14.0%
p 8028
14.0%
i 192
 
0.3%
u 192
 
0.3%
b 192
 
0.3%

uc_repo
Categorical

Constant  Missing 

Distinct1
Distinct (%)0.5%
Missing8028
Missing (%)97.7%
Memory size64.3 KiB
SAREC-Lab/sUAS-UseCases
192 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters4416
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSAREC-Lab/sUAS-UseCases
2nd rowSAREC-Lab/sUAS-UseCases
3rd rowSAREC-Lab/sUAS-UseCases
4th rowSAREC-Lab/sUAS-UseCases
5th rowSAREC-Lab/sUAS-UseCases

Common Values

ValueCountFrequency (%)
SAREC-Lab/sUAS-UseCases 192
 
2.3%
(Missing) 8028
97.7%

Length

2025-04-29T11:34:42.270414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-29T11:34:42.299342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sarec-lab/suas-usecases 192
100.0%

Most occurring characters

ValueCountFrequency (%)
s 768
17.4%
A 384
8.7%
C 384
8.7%
- 384
8.7%
S 384
8.7%
e 384
8.7%
a 384
8.7%
U 384
8.7%
R 192
 
4.3%
E 192
 
4.3%
Other values (3) 576
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4416
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 768
17.4%
A 384
8.7%
C 384
8.7%
- 384
8.7%
S 384
8.7%
e 384
8.7%
a 384
8.7%
U 384
8.7%
R 192
 
4.3%
E 192
 
4.3%
Other values (3) 576
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4416
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 768
17.4%
A 384
8.7%
C 384
8.7%
- 384
8.7%
S 384
8.7%
e 384
8.7%
a 384
8.7%
U 384
8.7%
R 192
 
4.3%
E 192
 
4.3%
Other values (3) 576
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4416
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 768
17.4%
A 384
8.7%
C 384
8.7%
- 384
8.7%
S 384
8.7%
e 384
8.7%
a 384
8.7%
U 384
8.7%
R 192
 
4.3%
E 192
 
4.3%
Other values (3) 576
13.0%

uc_temperature
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.96510949
Minimum0
Maximum1.4
Zeros192
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size64.3 KiB
2025-04-29T11:34:42.329350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6
Q11
median1
Q31
95-th percentile1.4
Maximum1.4
Range1.4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.24182293
Coefficient of variation (CV)0.25056528
Kurtosis4.3607922
Mean0.96510949
Median Absolute Deviation (MAD)0
Skewness-1.3361649
Sum7933.2
Variance0.058478331
MonotonicityNot monotonic
2025-04-29T11:34:42.370356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 4939
60.1%
0.8 841
 
10.2%
0.6 835
 
10.2%
1.2 789
 
9.6%
1.4 624
 
7.6%
0 192
 
2.3%
ValueCountFrequency (%)
0 192
 
2.3%
0.6 835
 
10.2%
0.8 841
 
10.2%
1 4939
60.1%
1.2 789
 
9.6%
1.4 624
 
7.6%
ValueCountFrequency (%)
1.4 624
 
7.6%
1.2 789
 
9.6%
1 4939
60.1%
0.8 841
 
10.2%
0.6 835
 
10.2%
0 192
 
2.3%

uc_top_p
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.77766423
Minimum0
Maximum1
Zeros192
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size64.3 KiB
2025-04-29T11:34:42.411539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.6
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.30505717
Coefficient of variation (CV)0.39227363
Kurtosis-0.32935852
Mean0.77766423
Median Absolute Deviation (MAD)0
Skewness-1.0449792
Sum6392.4
Variance0.09305988
MonotonicityNot monotonic
2025-04-29T11:34:42.454072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 4743
57.7%
0.8 833
 
10.1%
0.6 828
 
10.1%
0.2 817
 
9.9%
0.4 807
 
9.8%
0 192
 
2.3%
ValueCountFrequency (%)
0 192
 
2.3%
0.2 817
 
9.9%
0.4 807
 
9.8%
0.6 828
 
10.1%
0.8 833
 
10.1%
1 4743
57.7%
ValueCountFrequency (%)
1 4743
57.7%
0.8 833
 
10.1%
0.6 828
 
10.1%
0.4 807
 
9.8%
0.2 817
 
9.9%
0 192
 
2.3%

uc_actors
Real number (ℝ)

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0265207
Minimum0
Maximum19
Zeros31
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size64.3 KiB
2025-04-29T11:34:42.493269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q35
95-th percentile7
Maximum19
Range19
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.3736144
Coefficient of variation (CV)0.78427166
Kurtosis1.2689957
Mean3.0265207
Median Absolute Deviation (MAD)1
Skewness1.1529331
Sum24878
Variance5.6340453
MonotonicityNot monotonic
2025-04-29T11:34:42.542267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 3583
43.6%
4 976
 
11.9%
5 897
 
10.9%
3 752
 
9.1%
2 708
 
8.6%
6 538
 
6.5%
7 350
 
4.3%
8 160
 
1.9%
9 117
 
1.4%
10 51
 
0.6%
Other values (6) 88
 
1.1%
ValueCountFrequency (%)
0 31
 
0.4%
1 3583
43.6%
2 708
 
8.6%
3 752
 
9.1%
4 976
 
11.9%
5 897
 
10.9%
6 538
 
6.5%
7 350
 
4.3%
8 160
 
1.9%
9 117
 
1.4%
ValueCountFrequency (%)
19 1
 
< 0.1%
14 13
 
0.2%
13 16
 
0.2%
12 7
 
0.1%
11 20
 
0.2%
10 51
 
0.6%
9 117
 
1.4%
8 160
 
1.9%
7 350
4.3%
6 538
6.5%

uc_num_of_steps
Real number (ℝ)

Distinct51
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.2013382
Minimum1
Maximum101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.3 KiB
2025-04-29T11:34:42.603831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15.75
median8
Q311
95-th percentile20
Maximum101
Range100
Interquartile range (IQR)5.25

Descriptive statistics

Standard deviation5.7951168
Coefficient of variation (CV)0.62981238
Kurtosis14.377649
Mean9.2013382
Median Absolute Deviation (MAD)3
Skewness2.4393765
Sum75635
Variance33.583378
MonotonicityNot monotonic
2025-04-29T11:34:42.670389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 848
10.3%
6 847
10.3%
4 828
10.1%
7 670
 
8.2%
9 653
 
7.9%
10 642
 
7.8%
5 534
 
6.5%
11 500
 
6.1%
12 480
 
5.8%
3 362
 
4.4%
Other values (41) 1856
22.6%
ValueCountFrequency (%)
1 90
 
1.1%
2 241
 
2.9%
3 362
4.4%
4 828
10.1%
5 534
6.5%
6 847
10.3%
7 670
8.2%
8 848
10.3%
9 653
7.9%
10 642
7.8%
ValueCountFrequency (%)
101 1
 
< 0.1%
59 1
 
< 0.1%
54 1
 
< 0.1%
52 1
 
< 0.1%
50 1
 
< 0.1%
48 1
 
< 0.1%
45 1
 
< 0.1%
44 1
 
< 0.1%
43 1
 
< 0.1%
42 3
< 0.1%

uc_num_of_alt_scenarions
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1203163
Minimum0
Maximum8
Zeros7553
Zeros (%)91.9%
Negative0
Negative (%)0.0%
Memory size64.3 KiB
2025-04-29T11:34:42.723378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.46979675
Coefficient of variation (CV)3.9046808
Kurtosis38.249997
Mean0.1203163
Median Absolute Deviation (MAD)0
Skewness5.3120662
Sum989
Variance0.22070899
MonotonicityNot monotonic
2025-04-29T11:34:42.770380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 7553
91.9%
1 445
 
5.4%
2 155
 
1.9%
3 43
 
0.5%
4 20
 
0.2%
6 2
 
< 0.1%
8 1
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 7553
91.9%
1 445
 
5.4%
2 155
 
1.9%
3 43
 
0.5%
4 20
 
0.2%
5 1
 
< 0.1%
6 2
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
6 2
 
< 0.1%
5 1
 
< 0.1%
4 20
 
0.2%
3 43
 
0.5%
2 155
 
1.9%
1 445
 
5.4%
0 7553
91.9%

uc_num_of_err_scenarions
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.092092457
Minimum0
Maximum5
Zeros7733
Zeros (%)94.1%
Negative0
Negative (%)0.0%
Memory size64.3 KiB
2025-04-29T11:34:42.815653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.44028854
Coefficient of variation (CV)4.7809403
Kurtosis49.368811
Mean0.092092457
Median Absolute Deviation (MAD)0
Skewness6.4482618
Sum757
Variance0.193854
MonotonicityNot monotonic
2025-04-29T11:34:42.854606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 7733
94.1%
1 345
 
4.2%
2 58
 
0.7%
3 51
 
0.6%
4 22
 
0.3%
5 11
 
0.1%
ValueCountFrequency (%)
0 7733
94.1%
1 345
 
4.2%
2 58
 
0.7%
3 51
 
0.6%
4 22
 
0.3%
5 11
 
0.1%
ValueCountFrequency (%)
5 11
 
0.1%
4 22
 
0.3%
3 51
 
0.6%
2 58
 
0.7%
1 345
 
4.2%
0 7733
94.1%

seq_date
Date

Missing 

Distinct381
Distinct (%)5.1%
Missing766
Missing (%)9.3%
Memory size64.3 KiB
Minimum2013-12-31 00:00:00
Maximum2025-04-28 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-29T11:34:42.909386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:42.979382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

seq_origin
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.3 KiB
github
8028 
chatgpt
 
192

Length

Max length7
Median length6
Mean length6.0233577
Min length6

Characters and Unicode

Total characters49512
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowchatgpt
2nd rowchatgpt
3rd rowchatgpt
4th rowchatgpt
5th rowchatgpt

Common Values

ValueCountFrequency (%)
github 8028
97.7%
chatgpt 192
 
2.3%

Length

2025-04-29T11:34:43.042603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-29T11:34:43.073252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
github 8028
97.7%
chatgpt 192
 
2.3%

Most occurring characters

ValueCountFrequency (%)
t 8412
17.0%
g 8220
16.6%
h 8220
16.6%
i 8028
16.2%
u 8028
16.2%
b 8028
16.2%
c 192
 
0.4%
a 192
 
0.4%
p 192
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49512
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 8412
17.0%
g 8220
16.6%
h 8220
16.6%
i 8028
16.2%
u 8028
16.2%
b 8028
16.2%
c 192
 
0.4%
a 192
 
0.4%
p 192
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49512
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 8412
17.0%
g 8220
16.6%
h 8220
16.6%
i 8028
16.2%
u 8028
16.2%
b 8028
16.2%
c 192
 
0.4%
a 192
 
0.4%
p 192
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49512
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 8412
17.0%
g 8220
16.6%
h 8220
16.6%
i 8028
16.2%
u 8028
16.2%
b 8028
16.2%
c 192
 
0.4%
a 192
 
0.4%
p 192
 
0.4%

seq_repo
Text

Missing 

Distinct458
Distinct (%)5.7%
Missing192
Missing (%)2.3%
Memory size64.3 KiB
2025-04-29T11:34:43.206238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length61
Median length50
Mean length24.869706
Min length7

Characters and Unicode

Total characters199654
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowopenshift/lightspeed-service
2nd rowopenshift/lightspeed-service
3rd rowopenshift/lightspeed-service
4th rowopenshift/lightspeed-service
5th rowopenshift/lightspeed-service
ValueCountFrequency (%)
ryangolpayegani/us2sd_benchmark 1568
 
19.5%
caade/c3 128
 
1.6%
vanquang2002/server 100
 
1.2%
vanquang2002/server_ver1 98
 
1.2%
trongend123/huongsen 95
 
1.2%
ruben1132/pi5_23-24 84
 
1.0%
caade/edgeville 80
 
1.0%
cau-se-its/se_its_back-end 72
 
0.9%
madajaju/sabr 67
 
0.8%
madajaju/edgemere 62
 
0.8%
Other values (448) 5674
70.7%
2025-04-29T11:34:43.419166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 17264
 
8.6%
e 15611
 
7.8%
n 12791
 
6.4%
r 9494
 
4.8%
i 9460
 
4.7%
o 9056
 
4.5%
/ 8028
 
4.0%
l 6778
 
3.4%
t 6619
 
3.3%
- 6305
 
3.2%
Other values (56) 98248
49.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 199654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 17264
 
8.6%
e 15611
 
7.8%
n 12791
 
6.4%
r 9494
 
4.8%
i 9460
 
4.7%
o 9056
 
4.5%
/ 8028
 
4.0%
l 6778
 
3.4%
t 6619
 
3.3%
- 6305
 
3.2%
Other values (56) 98248
49.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 199654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 17264
 
8.6%
e 15611
 
7.8%
n 12791
 
6.4%
r 9494
 
4.8%
i 9460
 
4.7%
o 9056
 
4.5%
/ 8028
 
4.0%
l 6778
 
3.4%
t 6619
 
3.3%
- 6305
 
3.2%
Other values (56) 98248
49.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 199654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 17264
 
8.6%
e 15611
 
7.8%
n 12791
 
6.4%
r 9494
 
4.8%
i 9460
 
4.7%
o 9056
 
4.5%
/ 8028
 
4.0%
l 6778
 
3.4%
t 6619
 
3.3%
- 6305
 
3.2%
Other values (56) 98248
49.2%

seq_temperature
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.023017032
Minimum0
Maximum1.4
Zeros8028
Zeros (%)97.7%
Negative0
Negative (%)0.0%
Memory size64.3 KiB
2025-04-29T11:34:43.462662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1.4
Range1.4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.15161265
Coefficient of variation (CV)6.5869765
Kurtosis43.787626
Mean0.023017032
Median Absolute Deviation (MAD)0
Skewness6.6574626
Sum189.2
Variance0.022986395
MonotonicityNot monotonic
2025-04-29T11:34:43.505588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 8028
97.7%
1 119
 
1.4%
0.6 20
 
0.2%
0.8 20
 
0.2%
1.2 20
 
0.2%
1.4 13
 
0.2%
ValueCountFrequency (%)
0 8028
97.7%
0.6 20
 
0.2%
0.8 20
 
0.2%
1 119
 
1.4%
1.2 20
 
0.2%
1.4 13
 
0.2%
ValueCountFrequency (%)
1.4 13
 
0.2%
1.2 20
 
0.2%
1 119
 
1.4%
0.8 20
 
0.2%
0.6 20
 
0.2%
0 8028
97.7%

seq_top_p
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.018491484
Minimum0
Maximum1
Zeros8028
Zeros (%)97.7%
Negative0
Negative (%)0.0%
Memory size64.3 KiB
2025-04-29T11:34:43.548113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.12729905
Coefficient of variation (CV)6.8841987
Kurtosis49.919159
Mean0.018491484
Median Absolute Deviation (MAD)0
Skewness7.1123374
Sum152
Variance0.016205049
MonotonicityNot monotonic
2025-04-29T11:34:43.587141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 8028
97.7%
1 112
 
1.4%
0.2 20
 
0.2%
0.4 20
 
0.2%
0.6 20
 
0.2%
0.8 20
 
0.2%
ValueCountFrequency (%)
0 8028
97.7%
0.2 20
 
0.2%
0.4 20
 
0.2%
0.6 20
 
0.2%
0.8 20
 
0.2%
1 112
 
1.4%
ValueCountFrequency (%)
1 112
 
1.4%
0.8 20
 
0.2%
0.6 20
 
0.2%
0.4 20
 
0.2%
0.2 20
 
0.2%
0 8028
97.7%

seq_actors
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0821168
Minimum0
Maximum7
Zeros581
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size64.3 KiB
2025-04-29T11:34:43.623351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.61410314
Coefficient of variation (CV)0.56750172
Kurtosis26.090977
Mean1.0821168
Median Absolute Deviation (MAD)0
Skewness3.5832328
Sum8895
Variance0.37712266
MonotonicityNot monotonic
2025-04-29T11:34:43.665415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 6763
82.3%
2 641
 
7.8%
0 581
 
7.1%
3 164
 
2.0%
4 37
 
0.5%
7 20
 
0.2%
5 14
 
0.2%
ValueCountFrequency (%)
0 581
 
7.1%
1 6763
82.3%
2 641
 
7.8%
3 164
 
2.0%
4 37
 
0.5%
5 14
 
0.2%
7 20
 
0.2%
ValueCountFrequency (%)
7 20
 
0.2%
5 14
 
0.2%
4 37
 
0.5%
3 164
 
2.0%
2 641
 
7.8%
1 6763
82.3%
0 581
 
7.1%

seq_num_of_participats
Real number (ℝ)

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1655718
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.3 KiB
2025-04-29T11:34:43.710099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q36
95-th percentile9
Maximum19
Range18
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.3696497
Coefficient of variation (CV)0.4587391
Kurtosis5.6268769
Mean5.1655718
Median Absolute Deviation (MAD)1
Skewness1.7333204
Sum42461
Variance5.6152399
MonotonicityNot monotonic
2025-04-29T11:34:43.833313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
4 2018
24.5%
5 1633
19.9%
3 1017
12.4%
6 978
11.9%
7 916
11.1%
2 689
 
8.4%
8 347
 
4.2%
9 243
 
3.0%
10 121
 
1.5%
11 86
 
1.0%
Other values (6) 172
 
2.1%
ValueCountFrequency (%)
1 10
 
0.1%
2 689
 
8.4%
3 1017
12.4%
4 2018
24.5%
5 1633
19.9%
6 978
11.9%
7 916
11.1%
8 347
 
4.2%
9 243
 
3.0%
10 121
 
1.5%
ValueCountFrequency (%)
19 29
 
0.4%
16 20
 
0.2%
14 65
 
0.8%
13 29
 
0.4%
12 19
 
0.2%
11 86
 
1.0%
10 121
 
1.5%
9 243
 
3.0%
8 347
 
4.2%
7 916
11.1%

seq_num_of_alt
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.42396594
Minimum0
Maximum12
Zeros6292
Zeros (%)76.5%
Negative0
Negative (%)0.0%
Memory size64.3 KiB
2025-04-29T11:34:43.876644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0489584
Coefficient of variation (CV)2.4741573
Kurtosis31.953188
Mean0.42396594
Median Absolute Deviation (MAD)0
Skewness4.5647979
Sum3485
Variance1.1003137
MonotonicityNot monotonic
2025-04-29T11:34:43.921645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 6292
76.5%
1 1157
 
14.1%
2 418
 
5.1%
3 174
 
2.1%
4 89
 
1.1%
5 38
 
0.5%
6 25
 
0.3%
12 10
 
0.1%
10 9
 
0.1%
8 8
 
0.1%
ValueCountFrequency (%)
0 6292
76.5%
1 1157
 
14.1%
2 418
 
5.1%
3 174
 
2.1%
4 89
 
1.1%
5 38
 
0.5%
6 25
 
0.3%
8 8
 
0.1%
10 9
 
0.1%
12 10
 
0.1%
ValueCountFrequency (%)
12 10
 
0.1%
10 9
 
0.1%
8 8
 
0.1%
6 25
 
0.3%
5 38
 
0.5%
4 89
 
1.1%
3 174
 
2.1%
2 418
 
5.1%
1 1157
 
14.1%
0 6292
76.5%

seq_num_of_opt
Categorical

Imbalance 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size64.3 KiB
0
8067 
1
 
107
4
 
20
3
 
16
5
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8220
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row3
3rd row3
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8067
98.1%
1 107
 
1.3%
4 20
 
0.2%
3 16
 
0.2%
5 10
 
0.1%

Length

2025-04-29T11:34:43.971644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-29T11:34:44.005873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 8067
98.1%
1 107
 
1.3%
4 20
 
0.2%
3 16
 
0.2%
5 10
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 8067
98.1%
1 107
 
1.3%
4 20
 
0.2%
3 16
 
0.2%
5 10
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8220
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 8067
98.1%
1 107
 
1.3%
4 20
 
0.2%
3 16
 
0.2%
5 10
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8220
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 8067
98.1%
1 107
 
1.3%
4 20
 
0.2%
3 16
 
0.2%
5 10
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8220
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 8067
98.1%
1 107
 
1.3%
4 20
 
0.2%
3 16
 
0.2%
5 10
 
0.1%

seq_num_of_loop
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.079075426
Minimum0
Maximum8
Zeros7757
Zeros (%)94.4%
Negative0
Negative (%)0.0%
Memory size64.3 KiB
2025-04-29T11:34:44.042887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.42672051
Coefficient of variation (CV)5.3963733
Kurtosis150.62935
Mean0.079075426
Median Absolute Deviation (MAD)0
Skewness10.275896
Sum650
Variance0.1820904
MonotonicityNot monotonic
2025-04-29T11:34:44.084366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 7757
94.4%
1 384
 
4.7%
2 35
 
0.4%
4 19
 
0.2%
3 16
 
0.2%
8 9
 
0.1%
ValueCountFrequency (%)
0 7757
94.4%
1 384
 
4.7%
2 35
 
0.4%
3 16
 
0.2%
4 19
 
0.2%
8 9
 
0.1%
ValueCountFrequency (%)
8 9
 
0.1%
4 19
 
0.2%
3 16
 
0.2%
2 35
 
0.4%
1 384
 
4.7%
0 7757
94.4%

Interactions

2025-04-29T11:34:40.919353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:32.835777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:33.578129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:34.260185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:35.063189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:35.769539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:36.586676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:37.279291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:38.009260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:38.763262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:39.456893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:40.164351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:40.981571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:32.906398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:33.634315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:34.325181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:35.124834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:35.834460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:36.644970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:37.341297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:38.063862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:38.824411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:39.516490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:40.225497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:41.038093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:32.969033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:33.685909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:34.381049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:35.189453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:35.892236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:36.697508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:37.398283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:38.116436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:38.878158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:39.570717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:40.360376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:41.099965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:33.032023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:33.743829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:34.516761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:35.250567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:35.956621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:36.756450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:37.459921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:38.176372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:38.938222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:39.631871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:40.416072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:41.156883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:33.089554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:33.800349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:34.571772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:35.309067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:36.018786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:36.812687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:37.516914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:38.309285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:38.992230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:39.686005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:40.468548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:41.222997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:33.150554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:33.858861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:34.638096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:35.372212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:36.080494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:36.874254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:37.585446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:38.365239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:39.052020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:39.747937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:40.528796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:41.279778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:33.206730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:33.915874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:34.695506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:35.428456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:36.139499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:36.927188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:37.644838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:38.420414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:39.104765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:39.803179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:40.580817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:41.346773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:33.268997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:33.977876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:34.758798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:35.485306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:36.202168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:36.984890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:37.708881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:38.478187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:39.163855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:39.864951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:40.639017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:41.411536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:33.324971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:34.032643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:34.817049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:35.540566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:36.348573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:37.041557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:37.766093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:38.529381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:39.218052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:39.923429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:40.692939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:41.472275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:33.387383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:34.089014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:34.875940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:35.598489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:36.403916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:37.098061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:37.826614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:38.584984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:39.281268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:39.984441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:40.750872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:41.530275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:33.448066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:34.146317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:34.936940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:35.657164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:36.464557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:37.161799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:37.890001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:38.649024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:39.343308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:40.045258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:40.806131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:41.589292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:33.513071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:34.201799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:34.994917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:35.711537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:36.524585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:37.220677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:37.948014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:38.705258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:39.398567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:40.104351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:34:40.861444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-29T11:34:44.131366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
seq_actorsseq_num_of_altseq_num_of_loopseq_num_of_optseq_num_of_participatsseq_originseq_temperatureseq_top_puc_actorsuc_dateuc_num_of_alt_scenarionsuc_num_of_err_scenarionsuc_num_of_stepsuc_originuc_temperatureuc_top_p
seq_actors1.0000.1220.0330.0480.0560.3490.1400.1400.1270.2470.0470.0820.1040.349-0.043-0.038
seq_num_of_alt0.1221.0000.1670.1430.0430.016-0.035-0.0350.1220.0380.3670.1500.1040.016-0.022-0.006
seq_num_of_loop0.0330.1671.0000.3800.0820.019-0.031-0.0310.1260.0120.013-0.0190.1070.0190.0030.011
seq_num_of_opt0.0480.1430.3801.0000.1460.1340.0830.0700.0930.0940.0230.1230.0580.1340.0640.063
seq_num_of_participats0.0560.0430.0820.1461.0000.117-0.090-0.0900.3300.0830.011-0.0370.4600.1170.0220.021
seq_origin0.3490.0160.0190.1340.1171.0001.0001.0000.2731.0000.1510.7530.0170.9971.0001.000
seq_temperature0.140-0.035-0.0310.083-0.0901.0001.0001.0000.0170.7070.0950.5950.0161.000-0.296-0.292
seq_top_p0.140-0.035-0.0310.070-0.0901.0001.0001.0000.0170.7070.0950.5950.0161.000-0.296-0.292
uc_actors0.1270.1220.1260.0930.3300.2730.0170.0171.0000.1930.0130.0250.3230.273-0.041-0.058
uc_date0.2470.0380.0120.0940.0831.0000.7070.7070.1931.0000.1140.5330.0001.0000.7090.708
uc_num_of_alt_scenarions0.0470.3670.0130.0230.0110.1510.0950.0950.0130.1141.0000.516-0.0810.151-0.031-0.013
uc_num_of_err_scenarions0.0820.150-0.0190.123-0.0370.7530.5950.5950.0250.5330.5161.000-0.0420.753-0.167-0.152
uc_num_of_steps0.1040.1040.1070.0580.4600.0170.0160.0160.3230.000-0.081-0.0421.0000.017-0.047-0.053
uc_origin0.3490.0160.0190.1340.1170.9971.0001.0000.2731.0000.1510.7530.0171.0001.0001.000
uc_temperature-0.043-0.0220.0030.0640.0221.000-0.296-0.296-0.0410.709-0.031-0.167-0.0471.0001.0000.039
uc_top_p-0.038-0.0060.0110.0630.0211.000-0.292-0.292-0.0580.708-0.013-0.152-0.0531.0000.0391.000

Missing values

2025-04-29T11:34:41.692313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-29T11:34:41.797930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-29T11:34:42.026700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

uc_dateuc_originuc_repouc_temperatureuc_top_puc_actorsuc_num_of_stepsuc_num_of_alt_scenarionsuc_num_of_err_scenarionsseq_dateseq_originseq_reposeq_temperatureseq_top_pseq_actorsseq_num_of_participatsseq_num_of_altseq_num_of_optseq_num_of_loop
02020-12-19githubSAREC-Lab/sUAS-UseCases0.00.0216132025-04-27chatgptNaN0.61.008000
12020-12-19githubSAREC-Lab/sUAS-UseCases0.00.0216132025-04-27chatgptNaN1.00.2110130
22020-12-19githubSAREC-Lab/sUAS-UseCases0.00.0216132025-04-27chatgptNaN0.81.0011130
32020-12-19githubSAREC-Lab/sUAS-UseCases0.00.0216132025-04-27chatgptNaN1.00.407000
42020-12-19githubSAREC-Lab/sUAS-UseCases0.00.0216132025-04-27chatgptNaN1.01.006000
52020-12-19githubSAREC-Lab/sUAS-UseCases0.00.0216132025-04-27chatgptNaN1.00.6110130
62020-12-19githubSAREC-Lab/sUAS-UseCases0.00.0216132025-04-27chatgptNaN1.21.028000
72020-12-19githubSAREC-Lab/sUAS-UseCases0.00.0216132025-04-27chatgptNaN1.00.822000
82020-12-19githubSAREC-Lab/sUAS-UseCases0.00.0216132025-04-27chatgptNaN1.01.009130
92020-12-19githubSAREC-Lab/sUAS-UseCases0.00.039212025-04-27chatgptNaN0.61.033000
uc_dateuc_originuc_repouc_temperatureuc_top_puc_actorsuc_num_of_stepsuc_num_of_alt_scenarionsuc_num_of_err_scenarionsseq_dateseq_originseq_reposeq_temperatureseq_top_pseq_actorsseq_num_of_participatsseq_num_of_altseq_num_of_optseq_num_of_loop
82102025-04-29chatgptNaN1.21.019002025-01-29githubRyanGolpayegani/US2SD_Benchmark0.00.015000
82112025-04-29chatgptNaN0.61.045202025-01-29githubRyanGolpayegani/US2SD_Benchmark0.00.014100
82122025-04-29chatgptNaN1.00.4111102025-01-29githubRyanGolpayegani/US2SD_Benchmark0.00.014100
82132025-04-29chatgptNaN1.21.0112202025-01-29githubRyanGolpayegani/US2SD_Benchmark0.00.014100
82142025-04-29chatgptNaN0.81.0412002025-01-29githubRyanGolpayegani/US2SD_Benchmark0.00.014100
82152025-04-29chatgptNaN0.61.044002025-01-29githubRyanGolpayegani/US2SD_Benchmark0.00.016000
82162025-04-29chatgptNaN1.41.034002025-01-29githubRyanGolpayegani/US2SD_Benchmark0.00.016000
82172025-04-29chatgptNaN1.00.248002025-01-29githubRyanGolpayegani/US2SD_Benchmark0.00.014000
82182025-04-29chatgptNaN1.21.0422002025-01-29githubRyanGolpayegani/US2SD_Benchmark0.00.014000
82192025-04-29chatgptNaN1.41.048002025-01-29githubRyanGolpayegani/US2SD_Benchmark0.00.014000

Duplicate rows

Most frequently occurring

uc_dateuc_originuc_repouc_temperatureuc_top_puc_actorsuc_num_of_stepsuc_num_of_alt_scenarionsuc_num_of_err_scenarionsseq_dateseq_originseq_reposeq_temperatureseq_top_pseq_actorsseq_num_of_participatsseq_num_of_altseq_num_of_optseq_num_of_loop# duplicates
3642025-04-28chatgptNaN1.01.016002025-01-29githubRyanGolpayegani/US2SD_Benchmark0.00.01400019
3232025-04-28chatgptNaN1.01.014002018-07-17githubCAADE/C30.00.00400012
4792025-04-28chatgptNaN1.01.0511002025-01-29githubRyanGolpayegani/US2SD_Benchmark0.00.01500010
652025-04-28chatgptNaN0.81.016002025-01-29githubRyanGolpayegani/US2SD_Benchmark0.00.0140009
192025-04-28chatgptNaN0.61.016002025-01-29githubRyanGolpayegani/US2SD_Benchmark0.00.0140008
682025-04-28chatgptNaN0.81.018002025-01-29githubRyanGolpayegani/US2SD_Benchmark0.00.0150008
1632025-04-28chatgptNaN1.00.416002025-01-29githubRyanGolpayegani/US2SD_Benchmark0.00.0140008
2662025-04-28chatgptNaN1.00.816002025-01-29githubRyanGolpayegani/US2SD_Benchmark0.00.0140008
3262025-04-28chatgptNaN1.01.014002018-08-02githubCAADE/CloudLet0.00.0040008
4462025-04-28chatgptNaN1.01.046002025-01-29githubRyanGolpayegani/US2SD_Benchmark0.00.0140008